At 4Degrees we're focused on forming stronger and more authentic relationships between professionals. My co-founder and I both come from venture capital, so we've decided to focus our early efforts toward that mission in the VC and entrepreneur space.

One of the questions we've struggled with early on is how to intelligently match investors to startups. That type of matching has a lot of potential value- both to flag interesting relationships that already exist as well as to tee up introductions that could be mutually beneficial.

The first problem we ran into is that there's no good public structured dataset with this information. This isn't a deal breaker for us: we know a few sources of this information that aren't public and/or aren't structured and have the capabilities to wrangle that to our needs.

Probably more importantly, the traditional way of thinking of investor interests makes the obvious solutions in this space frustrating.

To illustrate, I'll start with the "good" case. Adam is a partner at Pritzker Group Venture Capital focused on healthcare technology investment. Silvervue is a startup providing solutions to hospitals. Adam is interested in Silvervue. All is good.

Now the "bad". Chris is another partner at PGVC focused on B2B investment. Outbound Engine is a marketing automation platform focused on the SMB market (small and medium sized businesses). Using the simple logic above (which predominates even the sophisticated approaches today), Chris should be interested in Outbound Engine. But he's not. While Chris does make some investments into SMB-targeted businesses, his true focus is on enterprise-targeted businesses. Even if a dataset does differentiate between SMB and enterprise investment (most don't), Chris does technically invest in SMB businesses. He just needs to see a more robust set of validating factors to lean in.

That brings us to the issue with current approaches: they assume investment interest is binary. As Chris' example shows, they're not.

In talking with Ben Blaiszik last week, we came across an interesting alternative. What if we treated investment interest as a continuous range, varying by sector? Perhaps instead of just being interested in SMB-targeted startups, Chris has a 0.3 interest value (in comparison to his 0.8 interest in enterprise-targeted companies).

Transforming the data in this way allows for much more intelligent and accurate matching of investor interests to startup focuses.

But why stop there? On the flip side, a startup's sector(s) could also be vectorized for more accurate representation.

The implications beyond this single matching problem are very interesting as well. This type of data structure allows us to make more powerful connections between investors and between entrepreneurs. And the structure could perhaps be extended to a multitude of other attributes: personal interests, industry expertise, skillsets. The list goes on and on.

At first blush, this type of structure presents a data collection challenge. Humans aren't really conditioned to apply gradations to their categories like this. But that doesn't trip us up for too long- the far more interesting application is categorization at scale. And when you think about the usage of probabilistic classifiers for automated categorization, this data structure is actually particularly well-suited. Rather than setting a binary threshold and converting a probability estimate to a 0 or 1, why not just score that probability directly as an element in the vector space?

I've been negligent in writing up my thoughts after finishing books. Part of it is due to not having read that many books... I got hung up about halfway through The Hero with a Thousand Faces and then began Techstars back in July. A couple of weeks back I finally gave up on Campbell and used the excused of travel and a need to unwind to start a new fantasy series. Thanks to the travel and a hunger for fantasy I hadn't fully appreciated, I tore through the four books in just a couple of weeks.

The Inda series was a nice dip back into fantasy literature. Not the best I've ever read, but definitely a top 25 series. The first book got off to a slow start and for most of it I didn't think I was going to continue past the first book. But the ending of that book was solid and Smith's writing progressively improved throughout the rest of the series. A bit strange given I understand she was an experienced author, but I was thrilled to see the progress.

Inda was neat in a number of ways. It started off in an academy setting and then evolved into maritime. I wasn't expecting the transition at all but it set a good scene (or collection thereof) for the rest of the series. It had a unique perspective to offer on mental disability, sexuality, the evolution of language/culture, and a number of other really interesting themes. It was probably one of the most enriching fantasy series I've read.

One of the great things about diving head first back into fantasy was a reminder of its restorative properties. Getting lost in a book feels so much less purposeless than watching TV or playing games often does. I constantly struggle with a feeling of listlessness from my time destressing; reading is a great alternative that avoids some of that.

I forgot to link to this post from a month back on how to form meaningful relationships with investors. The post is targeted toward entrepreneurs. The main idea is to seek out relationships with investors before you start raising a round. Not exactly novel advice, but it seemed particularly important to me as I mentally transitioned from VC back to founder.

Last week I finished up Nick Bostrom's Superintelligence: Paths, Dangers, Strategies. Coming into the book, I'd often heard it as the pessimist's response to Kurzweil's The Singularity is Near. I didn't really find that to be the case though: Bostrom certainly paints some scary pictures of potential futures as artificial intelligence develops, but he's not in denial about all of the positive potential. It's more that Bostrom felt there hadn't been a sufficient treatment of the downsides (and strategies to mitigate them) in the existing literature, so he sought to create that balance.

Getting through the read was a slog. It's probably one of the longest periods of time (a couple of months) I've spent on a book and still finished it. It's a topic I'm really passionate about too, more's the pity. The reality is that it's hard to relate to the topics that Bostrom digs into to a sufficient degree to justify spending time on the detail that he goes into. He spends 100 pages digging into the different structures of AGI systems and their relative merits and downsides vis-a-vis their capabilities and their potential to destroy humanity. I would be fascinated by the blog post. 100 pages is tough.

That being said, I don't think Superintelligence is a bad book. In fact, I think it serves as a great handbook to form a baseline for practitioners' future efforts to address the riskiness of developing AGI. After an initial read-through, the book may have lasting value as a reference guide when developers and researchers are diving into the actual development of these mitigation systems.

In all, I think Superintelligence is a must-read for any serious AI advocate. Most of the topics covered and the arguments presented won't be novel for someone who has spent time in the space, but it does provide a common language and frame of reference to drive future discussion. Relevant topics: superintelligence take-off scenarios, mediums (silicon, biological, swarm, etc.), types of systems, organizations that might pursue/achieve AGI.